#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_add.h"

#define CHECK_RET(cond, return_expr)     \
    do {                                 \
        if (!(cond)) {                   \
        return_expr;                     \
        }                                \
    } while (0)

#define LOG_PRINT(message, ...)          \
    do {                                 \
        printf(message, ##__VA_ARGS__);  \
    } while (0)

inline int64_t getShapeSize(const std::vector<int64_t>& shape) {
    int64_t shapeSize = 1;
    for (auto &i : shape) {
        shapeSize *= i;
    }
    return shapeSize;
}

int init(int32_t deviceId, aclrtContext* context, aclrtStream* stream) {
    aclError ret = aclrtSetDevice(deviceId);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSetDevice failed. ERROR: %d\n", ret); return ret);

    ret = aclInit(nullptr);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclInit failed. ERROR: %d\n", ret); return ret);

    ret = aclrtCreateContext(context, deviceId);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtCreateContext failed. ERROR: %d\n", ret); return ret);

    ret = aclrtSetCurrentContext(*context);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSetCurrentContext failed. ERROR: %d\n", ret); return ret);

    ret = aclrtCreateStream(stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtCreateStream failed. ERROR: %d\n", ret); return ret);

    return 0;
}

template <typename T>
int createAclTensor(const std::vector<T>& hostData, const std::vector<int64_t>& shape, 
                    void** deviceAddr, aclDataType dataType, aclTensor** tensor) {
    auto size = getShapeSize(shape) * sizeof(T);

    auto ret = aclrtMalloc(deviceAddr, size, ACL_MEM_MALLOC_HUGE_FIRST);    
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMalloc failed. ERROR: %d\n", ret); return ret);

    ret = aclrtMemcpy(*deviceAddr, size, hostData.data(), size, ACL_MEMCPY_HOST_TO_DEVICE);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMemcpy failed. ERROR: %d\n", ret); return ret);

    // 计算连续tensor的strides
    std::vector<int64_t> strides(shape.size(), 1);
    for (int64_t i = shape.size() - 2; i >= 0; i--) {
       strides[i] = shape[i + 1] * strides[i + 1];
    }

    // 调用aclCreateTensor接口创建aclTensor
    // aclTensor *aclCreateTensor(const int64_t *viewDims, uint64_t viewDimsNum, aclDataType dataType,
    //                                            const int64_t *stride, int64_t offset, aclFormat format,
    //                                            const int64_t *storageDims, uint64_t storageDimsNum,
    //                                            void *tensorData);
    *tensor = aclCreateTensor(shape.data(), shape.size(), dataType, strides.data(), 0, 
                    aclFormat::ACL_FORMAT_ND, shape.data(), shape.size(), *deviceAddr);
    return 0;
}

int main(int argc, char **argv) {
    //! 1. 初始化资源
    int32_t      deviceId = 0;
    aclrtContext context;
    aclrtStream  stream;
    aclError status = init(deviceId, &context, &stream);
    CHECK_RET(status == ACL_SUCCESS, LOG_PRINT("Init acl failed. ERROR: %d\n", status); return status);

    //! 2. 构造输入与输出
    std::vector<int64_t> selfShape  = {4, 2};
    std::vector<int64_t> otherShape = {4, 2};
    std::vector<int64_t> outShape   = {4, 2};

    void* selfDeviceAddr  = nullptr;
    void* otherDeviceAddr = nullptr;
    void* outDeviceAddr   = nullptr;

    aclTensor* self  = nullptr;
    aclTensor* other = nullptr;
    aclScalar* alpha = nullptr;
    aclTensor* out   = nullptr;

    std::vector<float> selfHostData  = {0, 1, 2, 3, 4, 5, 6, 7};
    std::vector<float> otherHostData = {1, 1, 1, 2, 2, 2, 3, 3};
    std::vector<float> outHostData   = {0, 0, 0, 0, 0, 0, 0, 0};
    float alphaValue = 1.2f;

    // 运算公式: out = self + alpha * other
    // aclnnStatus aclnnAddGetWorkspaceSize(const aclTensor* self, const aclTensor* other, const aclScalar* alpha,
    //                                  aclTensor* out, uint64_t* workspaceSize, aclOpExecutor** executor);

    // aclnnStatus aclnnAdd(void* workspace, uint64_t workspaceSize, aclOpExecutor* executor, aclrtStream stream);

    alpha   = aclCreateScalar(&alphaValue,   aclDataType::ACL_FLOAT);
    status  = createAclTensor(selfHostData,  selfShape,  &selfDeviceAddr,  aclDataType::ACL_FLOAT, &self);
    status  = createAclTensor(otherHostData, otherShape, &otherDeviceAddr, aclDataType::ACL_FLOAT, &other);
    status  = createAclTensor(outHostData,   outShape,   &outDeviceAddr,   aclDataType::ACL_FLOAT, &out);

    //! 3.调用CANN算子库API
    uint64_t workspaceSize = 0;
    aclOpExecutor* executor;
    status = aclnnAddGetWorkspaceSize(self, other, alpha, out, &workspaceSize, &executor);

    // 根据第一段接口计算出的workspaceSize申请device内存
    void* workspaceAddr = nullptr;
    if (workspaceSize > 0) {
        status = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
    }
    // 调用aclnnAdd第二段接口
    status = aclnnAdd(workspaceAddr, workspaceSize, executor, stream);
    status = aclrtSynchronizeStream(stream);

    //! 4.获取输出的值，将device侧内存上的结果拷贝至host侧，需要根据具体API的接口定义修改
    status = aclrtMemcpy(outHostData.data(), outHostData.size() * sizeof(outHostData[0]), 
                    outDeviceAddr, outHostData.size() * sizeof(float), ACL_MEMCPY_DEVICE_TO_HOST);

    for (int64_t i = 0; i < outHostData.size(); i++) {
        LOG_PRINT("result[%ld] is: %f\n", i, outHostData[i]);
    }

    //! 5.释放aclTensor和aclScalar，需要根据具体API的接口定义修改
    aclDestroyTensor(self);
    aclDestroyTensor(other);
    aclDestroyScalar(alpha);
    aclDestroyTensor(out);

    //! 6.释放device资源，需要根据具体API的接口定义修改
    aclrtFree(selfDeviceAddr);
    aclrtFree(otherDeviceAddr);
    aclrtFree(outDeviceAddr);
    if (workspaceSize > 0) {
        aclrtFree(workspaceAddr);
    }
    aclrtDestroyStream(stream);
    aclrtDestroyContext(context);
    aclrtResetDevice(deviceId);
    aclFinalize();

    return 0;
}